Human pose estimation aims to locate the human body parts and build human body
representation (eg, body skeleton) from input data such as images and videos. It has drawn …

Human pose estimation is a very active research field, stimulated by its important
applications in robotics, entertainment or health and sports sciences, among others …

Estimating 3D human poses from monocular videos is a challenging task due to depth
ambiguity and self-occlusion. Most existing works attempt to solve both issues by exploiting …

Recent transformer-based solutions have been introduced to estimate 3D human pose from
2D keypoint sequence by considering body joints among all frames globally to learn spatio …

Recently, transformer-based methods have gained significant success in sequential 2D-to-
3D lifting human pose estimation. As a pioneering work, PoseFormer captures spatial …

Transformer architectures have become the model of choice in natural language processing
and are now being introduced into computer vision tasks such as image classification, object …

Monocular 3D human pose estimation is quite challenging due to the inherent ambiguity
and occlusion, which often lead to high uncertainty and indeterminacy. On the other hand …

In this paper, a novel Diffusion-based 3D Pose estimation (D3DP) method with Joint-wise
reProjection-based Multi-hypothesis Aggregation (JPMA) is proposed for probabilistic 3D …

Most existing monocular 3D pose estimation approaches only focus on a single body part,
neglecting the fact that the essential nuance of human motion is conveyed through a concert …

We present a unified perspective on tackling various human-centric video tasks by learning
human motion representations from large-scale and heterogeneous data resources …